Automated Stent Detection in Intraluminal Imaging
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Solution Overview
Problem
Manual detection of stent apposition in tomographic imaging is time-consuming and prone to user error, making it challenging to accurately assess stent placement and identify mal-apposed stents in intraluminal imaging.
Innovation Solution
An automated method using image processing techniques, such as principal component analysis and regional covariance analysis, to detect stent locations within intraluminal images, eliminating the need for manual strut identification and improving detection accuracy by projecting input images onto pre-defined object spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection of stent struts is performed by cardiologists, then stent apposition can be assessed, but the process is time-consuming and prone to user error
Solution Approach 1:
The patent replaces the manual mechanical detection process (cardiologists visually locating stent struts in tomographic images) with an automated image processing system using principal component analysis and regional covariance analysis. This substitution eliminates human error and time consumption while maintaining or improving detection accuracy through algorithmic object space projection and covariance matrix comparison.
2Reliability
If manual detection of stent struts is performed, then stent placement can be evaluated, but user error reduces reliability
Solution Approach 1:
The patent replaces manual detection with an automated computational system that uses principal component analysis to create object spaces and regional covariance analysis to detect stent struts. This substitution improves reliability by eliminating user error, and while the algorithmic complexity increases, it provides consistent, reproducible results that override the simplicity of manual methods.
3Productivity
If automated image processing is used to detect stent locations, then detection speed is improved, but the complexity of the detection system increases
Solution Approach 1:
The patent replaces manual detection with an automated system using principal component analysis and regional covariance analysis, significantly improving detection speed. The complexity is managed through standardized algorithmic approaches that can be implemented in existing image processing platforms, making the complexity acceptable given the substantial productivity gain.
4Ease of operation
If manual strut identification is required, then the process is simple to understand, but it requires at least two stent struts to be located which is time-consuming
Solution Approach 1:
The patent replaces the simple but time-consuming manual process of locating at least two stent struts with an automated image processing system. The system automatically identifies all stent struts through object space projection and covariance analysis, eliminating the time required for manual searching while providing comprehensive detection beyond just two struts.
Data Source
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AI summary
This invention relates generally to the detection of objects, such as stents, within intraluminal images. A system and a method for automatically detecting a stent within an intraluminal image is disclosed, wherein a training set of pre-defined intraluminal images is generated, defined by a lumen border, a tissue space and a stent space; an A-line input intraluminal image is projected onto the stent space and tissue space around the lumen border; and the stent is detected within the input intraluminal image at locations based on a difference between the stent error and tissue error, wherein the tissue error and stent error along the A-line of the input intraluminal image is computed based on the input intraluminal image and the tissue space and stent space.